What is HLM 6.0?

HLM 6.0 is a statistical software package designed to estimate hierarchical linear models. Hierarchical linear models, also called multilevel, random-effects, or mixed models, are appropriate for data with a nested structure. Common examples are data that represent children clustered within schools, voters within districts, or workers within firms. In addition, hierarchical linear models may be appropriate for panel data (longitudinal data) where observations at different time points are treated as data nested within individuals.

HLM 6.0 is capable of estimating several types of parameters. Typical output includes estimates of fixed effects (regression coefficients) at the individual and grouping levels, as well as estimates of variance components describing the random coefficients. Additionally, HLM 6.0 produces a residual file you can use for testing model assumptions and finding empirical Bayes estimates for the random level-1 coefficients. HLM 6.0 can also estimate hierarchical generalized linear models for binary, ordered, categorical, and count-dependent variables.